Gambling neighbourhoods using DBSCAN and eps=500m
Load data
Load the geographical data (spatial points dataframe) containing the coordinates of gambling premises.
MinPts=2 with eps=500m
Load dataframe with coordinates and cluster assignments
res2_dbscan_eps500 <- readRDS("results/dbscan_results_eps0.45_MinPts2.rds")
# Create a data frame from the spatial points
df2_dbscan_eps500 <- create_df(res2_dbscan_eps500,xy)
head(df2_dbscan_eps500)
#> # A tibble: 6 × 5
#> LONG LAT cluster ID Pop
#> <dbl> <dbl> <fct> <int> <dbl>
#> 1 -2.10 57.1 0 7246 0
#> 2 -2.10 57.1 1 7929 2
#> 3 -2.10 57.1 1 4660 2
#> 4 -2.11 57.1 2 6787 2
#> 5 -2.11 57.1 2 5866 2
#> 6 -2.11 57.1 0 4658 0
dim(df2_dbscan_eps500)
#> [1] 8761 5Number of clusters (excluding noise):
#> [1] 1037
Number of noise points:
#> [1] 6296
Leaflet map for MinPts=2, eps=500m
MinPts=3 with eps=500m
Load dataframe with coordinates and cluster assignments
#> # A tibble: 6 × 5
#> LONG LAT cluster ID Pop
#> <dbl> <dbl> <fct> <int> <dbl>
#> 1 -2.10 57.1 0 7246 0
#> 2 -2.10 57.1 0 7929 0
#> 3 -2.10 57.1 0 4660 0
#> 4 -2.11 57.1 0 6787 0
#> 5 -2.11 57.1 0 5866 0
#> 6 -2.11 57.1 0 4658 0
#> [1] 8761 5
Number of clusters (excluding noise):
#> [1] 233
Number of noise points:
#> [1] 7904
Leaflet map for MinPts=3, eps=500m
MinPts=4 with eps=500m
Load dataframe with coordinates and cluster assignments
#> # A tibble: 6 × 5
#> LONG LAT cluster ID Pop
#> <dbl> <dbl> <fct> <int> <dbl>
#> 1 -2.10 57.1 0 7246 0
#> 2 -2.10 57.1 0 7929 0
#> 3 -2.10 57.1 0 4660 0
#> 4 -2.11 57.1 0 6787 0
#> 5 -2.11 57.1 0 5866 0
#> 6 -2.11 57.1 0 4658 0
#> [1] 8761 5
Number of clusters (excluding noise):
#> [1] 85
Number of noise points:
#> [1] 8348
Leaflet map for MinPts=4, eps=500m
MinPts=5 with eps=500m
Load dataframe with coordinates and cluster assignments
#> # A tibble: 6 × 5
#> LONG LAT cluster ID Pop
#> <dbl> <dbl> <fct> <int> <dbl>
#> 1 -2.10 57.1 0 7246 0
#> 2 -2.10 57.1 0 7929 0
#> 3 -2.10 57.1 0 4660 0
#> 4 -2.11 57.1 0 6787 0
#> 5 -2.11 57.1 0 5866 0
#> 6 -2.11 57.1 0 4658 0
#> [1] 8761 5
Number of clusters (excluding noise):
#> [1] 32
Number of noise points:
#> [1] 8560
Leaflet map for MinPts=5, eps=500m
MinPts=10 with eps=500m
Load dataframe with coordinates and cluster assignments
#> # A tibble: 6 × 5
#> LONG LAT cluster ID Pop
#> <dbl> <dbl> <fct> <int> <dbl>
#> 1 -2.10 57.1 0 7246 0
#> 2 -2.10 57.1 0 7929 0
#> 3 -2.10 57.1 0 4660 0
#> 4 -2.11 57.1 0 6787 0
#> 5 -2.11 57.1 0 5866 0
#> 6 -2.11 57.1 0 4658 0
#> [1] 8761 5
Number of clusters (excluding noise):
#> [1] 5
Number of noise points:
#> [1] 8711
Leaflet map for MinPts=10, eps=500m
MinPts=15 with eps=500m
Load dataframe with coordinates and cluster assignments
#> # A tibble: 6 × 5
#> LONG LAT cluster ID Pop
#> <dbl> <dbl> <fct> <int> <dbl>
#> 1 -2.10 57.1 0 7246 0
#> 2 -2.10 57.1 0 7929 0
#> 3 -2.10 57.1 0 4660 0
#> 4 -2.11 57.1 0 6787 0
#> 5 -2.11 57.1 0 5866 0
#> 6 -2.11 57.1 0 4658 0
#> [1] 8761 5
Number of clusters (excluding noise):
#> [1] 0
Number of noise points:
#> [1] 8761
No Leaflet map for MinPts=15, eps=500m
MinPts=20 with eps=500m
Load dataframe with coordinates and cluster assignments
#> # A tibble: 6 × 5
#> LONG LAT cluster ID Pop
#> <dbl> <dbl> <fct> <int> <dbl>
#> 1 -2.10 57.1 0 7246 0
#> 2 -2.10 57.1 0 7929 0
#> 3 -2.10 57.1 0 4660 0
#> 4 -2.11 57.1 0 6787 0
#> 5 -2.11 57.1 0 5866 0
#> 6 -2.11 57.1 0 4658 0
#> [1] 8761 5
Number of clusters (excluding noise):
#> [1] 0
Number of noise points:
#> [1] 8761
No Leaflet map for MinPts=20, eps=500m